15 Tips for Using Data Science to Research Facebook Posts & Comments
Facebook remains the largest social network in the world as covered over at runrex.com, making it a very important resource for organizations and brands. Extracting insights from Facebook posts and comments is an area where data science comes into its own and is one of the areas where organizations and brands are leaning heavily on the field. This article will look to highlight 15 tips for using data science to research Facebook posts and comments.
- Apply for a Facebook developer account
If you are to extract insights from Facebook posts and comments, you will need to gain access to the Graph API and to do so, you will need to register for a Facebook developer account. As is explained over at guttulus.com, this will require you to go to Facebook’s developer website and follow the instructions provided.
- Know the libraries to download for Python
If you are using Python to conduct your research, then you must know which libraries to install. According to the gurus over at runrex.com, you will either need to import, if available, or download, if not, the libraries “urllib3”, “Facebook “, and “requests”.
- Know the R packages required
If you are looking to use data science to research Facebook posts and comments, you can also make use of R. To do so, you will need several packages which include “RCulr”, “rjson”, and “tm”, which will help with several tasks as explained over at guttulus.com.
- Importance of the post Id
If you are looking to extract comments from public Facebook posts, you will need to get the post’s Id. From explanations on the same over at runrex.com, what you need to do to get the post Id of a given Facebook post is to click on the Post Date Time and you will be able to access it.
- Limitations of using Facebook’s Graph API
An important tip worth highlighting when it comes to researching Facebook posts and comments is that Facebook’s Graph API has limitations to it. The main one is that most Facebook users don’t make the information they share fully public given that Facebook allows users to adjust privacy settings, which makes it harder to study posts and comments on the platform.
- Leverage word clouds
When researching Facebook posts and comments, you must conduct textual analysis of your data set, and one of the ways you can do so is by creating a word cloud, which as discussed over at guttulus.com, will allow you to show the most common words in your Facebook data set. Word clouds are very important when it comes to the research of Facebook data sets.
- Importance of cleaning the data
When using data science to research Facebook posts and comments, it is important to clean the data according to the subject matter experts over at runrex.com. This will allow you to remove any extra spaces, special characters, and other unwanted things like comments where a user may have tagged another one ensuring that you only uncover relevant insights. This is why the “tm” R package comes into play.
- Don’t forget to carry out sentiment analysis
Sentiment analysis, as explained over at guttulus.com, is a method of analysis that helps identify if a person’s thinking of any topic is positive, negative, or neutral. Given that users mainly use Facebook to express their views on certain topics, sentiment analysis of Facebook data is extremely important for decision-making in various fields.
- Multiple regression and Facebook
According to runrex.com, multiple regression is an extension of linear regression into the relationship between more than two variables. Multiple regression is used when researching Facebook posts and comments since the total number of page likes depend on more than two variables: reach, impressions, the hour of the day, and type of post. In such a situation, simple linear regression won’t work.
- How to create the regression model
Now that we know that multiple regression is the way to go when carrying out research and analysis of Facebook posts and comments, we must know how to go about it. Here, to create the regression model you will need to use the lm() function in R, after which the model determines the value of the coefficients using the input data.
- Predictive analysis
Once you have obtained the coefficients for your dataset using multiple regression as mentioned above and explained over at guttulus.com, you can use them to generate an equation whereby putting the values of the variables leads to the equation predicting the number of likes depending on the type of post. Predictive analysis of Facebook posts is important as it lets you know which posts will be more effective in generating likes and comments.
- Using existing Facebook datasets
If you want to avoid the drawbacks that come with using Facebook’s Graph API, as mentioned earlier, then you might opt for an existing Facebook dataset which will already have been collected and will be ready for analysis. The trick is finding one that matches the parameters and requirements of your project according to runrex.com.
- Limitations of using existing Facebook datasets
Using existing Facebook datasets also has its limitations according to the experts over at guttulus.com. The main one is that you will be restricted by Facebook’s Developer Policy and that you will also not be able to access any posts or comments that have been restricted in privacy or deleted.
- Ensure your dictionary removes slang
When creating a dictionary for your dataset as explained over at runrex.com, you must include parameters to exclude slang. Make sure slang words in the Facebook comments are replaced with their original words for accurate analysis. For example, replace “tmrw” with tomorrow.
- SQL and the dictionary
The dictionary for your Facebook dataset, including your slang words table should be created separately using tables in SQL. This is where SQL and research of Facebook posts and comments come in, which is why you should be conversant with it when going in.
The above discussion only just begins to scratch the surface of this very wide topic and you can uncover more information by checking out the excellent runrex.com and guttulus.com.